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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A physics-informed machine learning model for surface roughness prediction in milling operations
Pengcheng Wu, Haicong Dai, Yufeng Li, et al.
The International Journal of Advanced Manufacturing Technology (2022) Vol. 123, Iss. 11-12, pp. 4065-4076
Closed Access | Times Cited: 18

Showing 18 citing articles:

Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning
Shi Zeng, Dechang Pi
Sensors (2023) Vol. 23, Iss. 10, pp. 4969-4969
Open Access | Times Cited: 14
Extreme learning machine oriented surface roughness prediction at continuous cutting positions based on monitored acceleration
Zequan Yao, Puyu Zhang, Ming Luo
Mechanical Systems and Signal Processing (2024) Vol. 219, pp. 111633-111633
Closed Access | Times Cited: 6
An in-process machined surface roughness classification using an ensemble learning algorithm based on extracted automated features from real-time surface images in milling process
Mulpur Sarat Babu
International Journal on Interactive Design and Manufacturing (IJIDeM) (2024) Vol. 18, Iss. 7, pp. 4499-4511
Closed Access | Times Cited: 5
Architecture-Guided Physics-Learned Machine Learning for Temperature Prediction in Laser-Assisted Turning Process
Mondi Rama Karthik, Thella Babu Rao
Lasers in Manufacturing and Materials Processing (2025)
Closed Access
Bayesian monitoring of machining processes using non-intrusive sensing and on-machine comparator measurement
Moschos Papananias
The International Journal of Advanced Manufacturing Technology (2025)
Open Access
Milling mechanism and surface roughness prediction model in ultrasonic vibration-assisted side milling of Ti–6Al–4 V
Weiwei Ming, Chongyan Cai, Zheng Ma, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 131, Iss. 5-6, pp. 2279-2293
Closed Access | Times Cited: 9
A data-driven method for prediction of surface roughness with consideration of milling tool wear
Zhao Zhang, Long Jia, Ming Luo, et al.
The International Journal of Advanced Manufacturing Technology (2024) Vol. 134, Iss. 9-10, pp. 4271-4282
Closed Access | Times Cited: 2
Towards AI driven surface roughness evaluation in manufacturing: a prospective study
Sourish Ghosh, Ricardo Knoblauch, Mohamed El Mansori, et al.
Journal of Intelligent Manufacturing (2024)
Open Access | Times Cited: 2
Remaining useful lifetime prediction for milling blades using a fused data prediction model (FDPM)
Teemu Mäkiaho, Jouko Laitinen, Mikael Nuutila, et al.
Journal of Intelligent Manufacturing (2024) Vol. 35, Iss. 8, pp. 4035-4054
Open Access | Times Cited: 1
A Generic Multi-Objective Optimization of Machining Processes Using an End-to-End Evolutionary Algorithm
Xun Cheng, Pengcheng Wu
Machines (2024) Vol. 12, Iss. 9, pp. 635-635
Open Access | Times Cited: 1
Experimental study of wet and dry milling effect on surface roughness of TC4 titanium alloy
Y.S. Li, Shuncai Li, Yang Li
Machining Science and Technology (2024), pp. 1-22
Closed Access | Times Cited: 1
A real time condition based sustainable maintenance method for milling process
Pengcheng Wu, Min Xia, Limei Hu
Journal of Cleaner Production (2023) Vol. 434, pp. 140284-140284
Closed Access | Times Cited: 2
Deep learning–based inline monitoring approach of mold coating thickness for Al-Si alloy permanent mold casting
Fangtian Deng, Xingyu Rui, Shuang Lu, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 130, Iss. 1-2, pp. 565-573
Open Access | Times Cited: 1
Influence of different machining methods on the surface roughness of TC4: dry milling, water-based fluid wet milling, and foam spray milling
Youyong Li, Shuncai Li, Xin Wang
Transactions of the Canadian Society for Mechanical Engineering (2024) Vol. 48, Iss. 3, pp. 447-458
Closed Access
Classification of surface roughness for milled A6061 aluminum alloy based on depth map models with convolutional neural networks
Tran Thi Hien, Songyun Deng
Forschung im Ingenieurwesen (2024) Vol. 88, Iss. 1
Closed Access
Physics Guided Neural Networks with Knowledge Graph
Kishor Datta Gupta, Sunzida Siddique, Roy George, et al.
Digital (2024) Vol. 4, Iss. 4, pp. 846-865
Open Access
NextG manufacturing − New extreme manufacturing paradigm from the temporal perspective
Limei Hu, Y. B. Guo, Ivan Seskar, et al.
Journal of Manufacturing Systems (2024) Vol. 77, pp. 418-431
Closed Access
Adaptive Hybrid Prediction Model for Adapting to Data Distribution Shifts in Machining Quality Prediction
Feng Li, Xu Yang, Jie Gao, et al.
Measurement Science and Technology (2024) Vol. 36, Iss. 1, pp. 016022-016022
Closed Access
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